Generally, the bit rate required to pass an audio and/or video signal with sufficient quality is an important parameter in telecommunications. In order to reduce this parameter and thus increase the number of possible communications via one and the same network, audio coders have been developed in particular for compressing the quantity of information required to transmit a signal.
Certain coders make it possible to achieve particularly high information compression factors. Such coders generally use advanced information modeling and quantization techniques. Thus, these coders only transmit models or partial data of the signal.
The decoded signal, although it is not identical to the original signal (since part of the information has not been transmitted on account of the quantization operation), nevertheless remains very close to the original signal (at least from the perception point of view). The difference, in the mathematical sense, between the decoded signal and the original signal is then called “quantization noise”.
Signal compression processings are often designed so as to minimize quantization noise and, in particular, to render this quantization noise as inaudible as possible when the processing of an audio signal is involved. Thus, techniques exist which take into account the psycho-acoustic characteristics of hearing, with the aim of “masking” this noise. However, to obtain the lowest possible bit rates, the quantization noise may sometimes be difficult (or indeed impossible) to mask totally, thereby, in certain circumstances, degrading the intelligibility and/or the quality of the signal.
In order to reduce this quantization noise and hence improve quality, two families of techniques can be used on decoding.
It is possible, firstly, to use an adaptive post-filter, of the type described in the article by Chen and Gersho:
“Adaptive post filtering for quality enhancement of coded speech”, IEEE Transactions on Speech and Audio Processing, Vol. 3, No. 1, Jan. 1995, pages 59-71, and employed in particular in the speech decoders of CELP (“Code Excited Linear Prediction”) type.
This involves performing a filtering which improves subjective quality by attenuating the signal in the zones where the quantization noise is most audible (in particular between the formants and the harmonics of fundamental period or “pitch”). Current adaptive post-filters afford good results for speech signals, but less good results for other types of signals (music signals, for example).
Another processing family is aimed at the conventional noise reduction processings which distinguish the useful signal from spurious noise and which can be applied as post-processing to reduce the quantization noise after decoding. This type of processing makes it possible at the origin to reduce the noise related to the signal capture environment and it is often used for speech signals. However, it is impossible to make the processing transparent in relation to the noise related to the sound pick-up environment, thereby posing a problem for music signal coding, in particular. Thus, in coding/decoding, one might want to transmit the “atmospheric” noise and it is then desirable for the noise reduction not to apply to this type of “atmospheric” noise but solely to the quantization noise, in particular in the context of post-processing on decoding aimed at reducing quantization noise.
Nevertheless, these various types of quantization noise reduction methods deform the signal to a greater or lesser extent. For example, the use of a post-filter (denoising) which would be too aggressive for the speech signal would make it possible to completely eliminate the quantization noise but the voice sound obtained would seem less natural and/or muffled. Optimization of these various types of methods is therefore difficult and it is appropriate systematically to find a compromise between:                the effectiveness of suppression of the quantization noise, and        the conservation of the properties of the initial signal, in particular in terms of natural or unnatural aspect.        